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How P&G and American Express Are Approaching AI

harvardbusiness.org 31 March 2017
There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies — which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of “chief cognitive officer” roles, and certainly many firms are rushing to hire experts with deep learning expertise. “New and different” is the ethos of the day.

How to Win with Automation (Hint: It's Not Chasing Efficiency)

harvardbusiness.org 30 March 2017
In 1900, 30 million people in the United States were farmers. By 1990 that number had fallen to under 3 million even as the population more than tripled. So, in a matter of speaking, 90% of American agriculture workers lost their jobs, mostly due to automation. Yet somehow, the 20th century was still seen as an era of unprecedented prosperity.

Why CIOs Make Great Board Directors

harvardbusiness.org 15 March 2017
According to Korn Ferry unpublished data, there has been a 74% increase in the number of CIOs serving on Fortune 100 boards in the past two years. It’s no wonder CIOs are the fastest-growing addition to the boardroom: They can help address a host of issues of crucial importance to boards, including using technologies to create operational efficiencies and competitive advantage; identifying opportunities related to cloud computing, digitization, and data; addressing threats and risks associated with information security; and using their experience and judgment to oversee, question, and provide input on technology budgets.

What Blockchain Means for the Sharing Economy

harvardbusiness.org 15 March 2017
Look at the modus operandi of today’s internet giants — such as Google, Facebook, Twitter, Uber, or Airbnb — and you’ll notice they have one thing in common: They rely on the contributions of users as a means to generate value within their own platforms. Over the past 20 years the economy has progressively moved away from the traditional model of centralized organizations, where large operators, often with a dominant position, were responsible for providing a service to a group of passive consumers. Today we are moving toward a new model of increasingly decentralized organizations, where large operators are responsible for aggregating the resources of multiple people to provide a service to a much more active group of consumers. This shift marks the advent of a new generation of “dematerialized” organizations that do not require physical offices, assets, or even employees.

How Blockchain Is Changing Finance

harvardbusiness.org 1 March 2017
Our global financial system moves trillions of dollars a day and serves billions of people. But the system is rife with problems, adding cost through fees and delays, creating friction through redundant and onerous paperwork, and opening up opportunities for fraud and crime. To wit, 45% of financial intermediaries, such as payment networks, stock exchanges, and money transfer services, suffer from economic crime every year; the number is 37% for the entire economy, and only 20% and 27% for the professional services and technology sectors, respectively. It's no small wonder that regulatory costs continue to climb and remain a top concern for bankers. This all adds cost, with consumers ultimately bearing the burden. It begs the question: Why is our financial system so inefficient? First, because it's antiquated, a kludge of industrial technologies and paper-based processes dressed up in a digital wrapper. Second, because it's centralized, which makes it resistant to change and vulne

Call Length Is the Worst Way to Measure Customer Service

harvardbusiness.org 22 February 2017
Practitioners and pundits alike have long debated which metric is best for assessing the performance of a service organization. Is the silver bullet customer satisfaction, net promoter score, customer effort score, or some other measure? While this debate is unlikely to be settled anytime soon, we’d submit that there’s no question what the worst metric is for service: average handle time (AHT), which is principally a measure of call length, or, more simply, talk time.

The Rise of AI Makes Emotional Intelligence More Important

harvardbusiness.org 15 February 2017
feb17-15-157640301 The booming growth of machine learning and artificial intelligence (AI), like most transformational technologies, is both exciting and scary. It's exciting to consider all the ways our lives may improve, from managing our calendars to making  medical diagnoses, but it's scary to consider the social and personal implications - and particularly the implications for our careers. As machine learning continues to grow, we all need to develop new skills in order to differentiate ourselves. But which ones? It's long been known that AI and automation/robotics will change markets and workforces. Self-driving cars will force over three thousand truck drivers to seek new forms of employment, and robotic production lines like Tesla's will continue to eat away at manufacturing jobs, which are currently at 12 million and falling. But this is just the beginning of the disruption. As AI improves, which is happening quickly, a much broader set of 'thinking' rather than 'doing' j

How Technology Can Help Close the Gender Gap

harvardbusiness.org 25 January 2017
What is the experience of a woman in corporate America today? She probably hears a lot about diversity initiatives from the leadership of her company, but she probably has precious little to show it, save a smattering of diversity days, mentoring programs, employee advocacy groups, and other gender programs. Boards and senior leadership at her company remain stubbornly male, and women continue to earn less than men for comparable work.

Why We Ask Every New Employee to Code an App Their First Week on the Job

harvardbusiness.org 23 December 2016
In the early days of Twilio, the cloud communications platform company where I work, the company’s founders decided that priority number one was customer empathy. That’s not unique, of course. But as a company created by developers for developers, early on we all had the job of talking to customers constantly. When the company was younger, we worked in a small office where everyone knew everything that was going on.

What It Will Take for Us to Trust AI

harvardbusiness.org 29 November 2016
The early days of artificial intelligence have been met with some very public hand wringing. Well-respected technologists and business leaders have voiced their concerns over the (responsible) development of AI. And Hollywood’s appetite for dystopian AI narratives appears to be bottomless. This is not unusual, nor is it unreasonable. Change, technological or otherwise, always excites the imagination. And it often makes us a little uncomfortable.

Bots That Can Talk Will Help Us Get More Value from Analytics

harvardbusiness.org 24 November 2016
Over the past few years, much has been made of the rise of big data. And yet research from TDWI states that at organizations where 50% of employees have access to business intelligence tools, only 20% of that group actually use them. Part of the problem is that systems are often hard to use. Another challenge is low rates of data literacy.

The Simple Economics of Machine Intelligence

harvardbusiness.org 17 November 2016
The year 1995 was heralded as the beginning of the “New Economy.” Digital communication was set to upend markets and change everything. But economists by and large didn’t buy into the hype. It wasn’t that we didn’t recognize that something changed. It was that we recognized that the old economics lens remained useful for looking at the changes taking place. The economics of the “New Economy” could be described at a high level: Digital technology would cause a reduction in the cost of search and communication. This would lead to more search, more communication, and more activities that go together with search and communication. That’s essentially what happened.

Hiring Your First Chief AI Officer

harvardbusiness.org 11 November 2016
A hundred years ago electricity transformed countless industries; 20 years ago the internet did, too. Artificial intelligence is about to do the same. To take advantage, companies need to understand what AI can do and how it relates to their strategies. But how should you organize your leadership team to best prepare for this coming disruption? Follow history.

What Artificial Intelligence Can and Can't Do Right Now

harvardbusiness.org 9 November 2016
Many executives ask me what artificial intelligence can do. They want to know how it will disrupt their industry and how they can use it to reinvent their own companies. But lately the media has sometimes painted an unrealistic picture of the powers of AI. (Perhaps soon it will take over the world!) AI is already transforming web search, advertising, e-commerce, finance, logistics, media, and more. As the founding lead of the Google Brain team, former director of the Stanford Artificial Intelligence Laboratory, and now overall lead of Baidu’s AI team of some 1,200 people, I’ve been privileged to nurture many of the world’s leading AI groups and have built many AI products that are used by hundreds of millions of people. Having seen AI’s impact, I can say: AI will transform many industries. But it’s not magic. To understand the implications for your business, let’s cut through the hype and see what AI really is doing today.

You Don't Need Big Data - You Need the Right Data

harvardbusiness.org 3 November 2016
Marion Barraud for HBR The term 'big data' is ubiquitous. With exabytes of information flowing across broadband pipes, companies compete to claim the biggest, most audacious data sets. And businesses of all varieties - old and new, industrial and digital, big and small - are getting into the game. Masses of social, weather, and government data are being leveraged to predict supply chain outages. Enormous amounts of user data are being harnessed at scale to identify individuals among a sea of website clicks. And companies are even starting to leverage huge quantities of text exchanges to build algorithms capable of having conversations with customers. But the reality is that our relentless focus on the importance of big data is often misleading. Yes, in some situations, deriving value from data requires having an immense amount of that data. But the key for innovators across industries is that the size of the data isn't the most critical factor

The Competitive Landscape for Machine Intelligence

harvardbusiness.org 2 November 2016
Three years ago, our venture capital firm began studying startups in artificial intelligence. AI felt misunderstood, burdened by expectations from science fiction, and so for the last two years we’ve tried to capture the most-important startups in the space in a one-page landscape. (We prefer the more neutral term “machine intelligence” over “AI.”)

How Artificial Intelligence Will Redefine Management

harvardbusiness.org 2 November 2016
Many alarms have sounded on the potential for artificial intelligence (AI) technologies to upend the workforce, especially for easy-to-automate jobs. But managers at all levels will have to adapt to the world of smart machines. The fact is, artificial intelligence will soon be able to do the administrative tasks that consume much of managers’ time faster, better, and at a lower cost.

Machine Learning Is No Longer Just for Experts

harvardbusiness.org 26 October 2016
If you’re not using deep learning already, you should be. That was the message from legendary Google engineer Jeff Dean at the end of his keynote earlier this year at a conference on web search and data mining. Dean was referring to the rapid increase in machine learning algorithms’ accuracy, driven by recent progress in deep learning, and the still untapped potential of these improved algorithms to change the world we live in and the products we build.

New Evidence Shows Search Engines Reinforce Social Stereotypes

harvardbusiness.org 20 October 2016
In April, an MBA student named Bonnie Kamona, reported that a Google image search for “unprofessional hair for work” produced a set of images that almost exclusively depicted women of color. In contrast, her search for “professional hair” delivered images of white women. Two months later, Twitter user Ali Kabir’s report on an image search for “three black teenagers” resulted in a good deal of mug shots, while “three white teenagers” retrieved images of young people having fun.

Finding the Sweet Spot Between Mass Market and Premium

harvardbusiness.org 19 October 2016
Persuading consumers to pay more for a product by introducing some kind of “premium” element into it has always been a challenging task—but it was one that big, established brands had managed with a reasonable amount of success until recent years. For example, Gillette has successfully encouraged consumers to trade up again and again by continually introducing razors with the latest and greatest shaving technology. A decade ago, the Mach 3 razor was Gillette’s premium offering for men, until the Fusion line was launched in 2006 at a 40% price increase, followed by the Fusion ProGlide in 2010 and the Fusion Proshield Flexball in 2016—to name a few of the brand’s major releases.

Leaders Need Different Skills to Thrive in Tech

harvardbusiness.org 17 October 2016
You accept your first job as a manager in a fast growth tech company, thinking: “How much different could this be from my former company—a financial services firm? Management is management, right?” But by the end of the first month you feel confused and disoriented. A series of jarring experiences have taught you that...

Why Better Technology Can Be Slower to Take Off

harvardbusiness.org 12 October 2016
Find your place on the automation frontier.

The 'Maximize Profits' Trap in Decision Making

harvardbusiness.org 19 September 2016
What's the right way to make hard business decisions? We all know the standard answers: Obey the law and do whatever maximizes profits or produces the greatest shareholder value. This logic and the institutions that reinforce it, like competitive markets and the rule of law, have transformed the world and lifted billions of people from poverty. But, for gray area decisions, the standard answer isn't the right answer. Gray areas are situations with high uncertainty and serious human stakes. In these situations, you have to look hard at the economics, but you can't stop there. You have to approach these problems as a manager and do the best analysis you can, including hard-headed financial analysis. In the end, however, you have to rely on your judgment and resolve these problems as a human being. In these cases, running the numbers and grasping what they tell you is important, but it isn't enough.

Global Companies Need to Adopt Agile Pricing in Emerging Markets

harvardbusiness.org 19 September 2016
One day in December 2014, Sergey, the Russia general manager for a multinational consumer goods company, was up early in the morning, watching the ruble's value slide by the minute. As the currency was crashing, he found himself facing a painful dilemma: either raise prices to recoup the losses and hit his annual target - set in U.S. dollars - or wait it out for another two months and hope that the ruble recovers, since that would give him a leg up on his competitors. But with the currency changing every day, how much of a price increase should he consider? The 30% that the ruble had dropped since his last quarterly review? Or should it be more, to compensate for the likelihood of further depreciation to come? That would make his product unaffordable for most of his core customers, and they would almost certainly switch to his competitors' cheaper alternative. There was no good solution.

A Quick Guide to Value-Based Pricing

harvardbusiness.org 9 August 2016
In my 15-plus years of working with companies & teaching courses on pricing strategies to MBA students, I have found value-based pricing (also known as 'value pricing') to be the most commonly discussed concept that's also the most misunderstood one. It creates more confusion among marketers, even many pricing experts, than any other pricing concept. What is more, these misconceptions often lead companies to shy away from using it, instead settling for cost-based or other pricing methods that leave money on the table.

Figuring Out How IT, Analytics, and Operations Should Work Together

harvardbusiness.org 3 August 2016
A new set of relationships is being formed within companies around how people working in data, analytics, IT, and operations teams work together. Is there a 'right' way to structure these relationships? Data and analytics represent a blurring of the traditional lines of demarcation between the scope of IT and the responsibilities of operating divisions. Consider the core mission of the modern IT department: Taking in all the technology 'mess' (often from several different divisions), developing the necessary competencies, and delivering savings and efficiency to the company. Many IT organizations, having achieved this original mission, now are turning to the next thing, which is innovation. Enter data and analytics, which provide an opportunity for such innovation. However, data traditionally is owned by the business, and analytics is valuable only to the extent that it is used to make business decisions, again 'owned' by the business.

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